






'''simple docstring'''







from typing import Dict, Optional

import numpy as np

import datasets


__lowerCAmelCase        =							"\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"

__lowerCAmelCase        =							"\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n"

__lowerCAmelCase        =							"\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"


def       __UpperCamelCase					(      lowercase_       :  Tuple   ,       lowercase_       :  Tuple   ,       lowercase_       :  Optional[Any]   ,       lowercase_       :  bool   ,       lowercase_       :  Optional[Dict[int, int]] = None   ,       lowercase_       :  bool = False   ,       ):
       """simple docstring"""




       if label_map is not None:
              for old_id, new_id in label_map.items():
                     a_						     =			new_id

    # turn into Numpy arrays
       a_						     =			np.array(lowercase_   )
       a_						     =			np.array(lowercase_   )

       if reduce_labels:
              a_						     =			255
              a_						     =			label - 1
              a_						     =			255

       a_						     =			label != ignore_index
       a_						     =			np.not_equal(lowercase_   ,       lowercase_   )
       a_						     =			pred_label[mask]
       a_						     =			np.array(lowercase_   )[mask]

       a_						     =			pred_label[pred_label == label]

       a_						     =			np.histogram(lowercase_   ,       bins=lowercase_   ,       range=(0, num_labels - 1)   )[0]
       a_						     =			np.histogram(lowercase_   ,       bins=lowercase_   ,       range=(0, num_labels - 1)   )[0]
       a_						     =			np.histogram(lowercase_   ,       bins=lowercase_   ,       range=(0, num_labels - 1)   )[0]

       a_						     =			area_pred_label + area_label - area_intersect

       return area_intersect, area_union, area_pred_label, area_label


def       __UpperCamelCase					(      lowercase_       :  List[str]   ,       lowercase_       :  List[Any]   ,       lowercase_       :  Dict   ,       lowercase_       :  bool   ,       lowercase_       :  Optional[Dict[int, int]] = None   ,       lowercase_       :  bool = False   ,       ):
       """simple docstring"""




       a_						     =			np.zeros((num_labels,)   ,       dtype=np.floataa   )
       a_						     =			np.zeros((num_labels,)   ,       dtype=np.floataa   )
       a_						     =			np.zeros((num_labels,)   ,       dtype=np.floataa   )
       a_						     =			np.zeros((num_labels,)   ,       dtype=np.floataa   )
       for result, gt_seg_map in zip(lowercase_   ,       lowercase_   ):
              a_     ,	a_     ,	a_     ,	a_						     =			intersect_and_union(
                  lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   )
              total_area_intersect += area_intersect
              total_area_union += area_union
              total_area_pred_label += area_pred_label
              total_area_label += area_label
       return total_area_intersect, total_area_union, total_area_pred_label, total_area_label


def       __UpperCamelCase					(      lowercase_       :  int   ,       lowercase_       :  str   ,       lowercase_       :  List[Any]   ,       lowercase_       :  bool   ,       lowercase_       :  Optional[int] = None   ,       lowercase_       :  Optional[Dict[int, int]] = None   ,       lowercase_       :  bool = False   ,       ):
       """simple docstring"""




       a_     ,	a_     ,	a_     ,	a_						     =			total_intersect_and_union(
           lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   )

       # compute metrics
       a_						     =			{}

       a_						     =			total_area_intersect.sum() / total_area_label.sum()
       a_						     =			total_area_intersect / total_area_union
       a_						     =			total_area_intersect / total_area_label

       a_						     =			np.nanmean(lowercase_   )
       a_						     =			np.nanmean(lowercase_   )
       a_						     =			all_acc
       a_						     =			iou
       a_						     =			acc

       if nan_to_num is not None:
              a_						     =			{metric: np.nan_to_num(lowercase_   ,       nan=lowercase_   ) for metric, metric_value in metrics.items()}

       return metrics




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,       _KWARGS_DESCRIPTION       )
class   __SCREAMING_SNAKE_CASE       (datasets.Metric       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         return datasets.MetricInfo(
             description=_DESCRIPTION   ,	citation=_CITATION   ,	inputs_description=_KWARGS_DESCRIPTION   ,	features=datasets.Features(
                 # 1st Seq - height dim, 2nd - width dim
                 {
                     'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'	)	)	),
                     'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'	)	)	),
                 }	)   ,	reference_urls=[
                 'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
             ]   ,	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = False   ,	):






         """simple docstring"""


         a_						     =			mean_iou(
             results=UpperCamelCase__   ,	gt_seg_maps=UpperCamelCase__   ,	num_labels=UpperCamelCase__   ,	ignore_index=UpperCamelCase__   ,	nan_to_num=UpperCamelCase__   ,	label_map=UpperCamelCase__   ,	reduce_labels=UpperCamelCase__   ,	)
         return iou_result












'''simple docstring'''







def       __UpperCamelCase					(      lowercase_       :  int = 1_000_000   ):
       """simple docstring"""




       a_						     =			set(range(3   ,       lowercase_   ,       2   )   )
       primes.add(2   )
       for p in range(3   ,       lowercase_   ,       2   ):
              if p not in primes:
                     continue
              primes.difference_update(set(range(p * p   ,       lowercase_   ,       lowercase_   )   )   )

       a_						     =			[float(lowercase_   ) for n in range(limit + 1   )]

       for p in primes:
              for n in range(lowercase_   ,       limit + 1   ,       lowercase_   ):
                     phi[n] *= 1 - 1 / p

       return int(sum(phi[2:]   )   )


if __name__ == "__main__":
  print(f"""{solution() = }""")




